Do the eigenvectors represent the original features?
The principal components (eigenvectors) correspond to the direction
  (in the original n-dimensional space) with the greatest variance in
  the data.
  
  The corresponding eigenvalue is a number that indicates how much
  variance there is in the data along that eigenvector (or principal
  component).


Thus, feature 2 is the most important (based on eigenvalue alone). Then feature 1. The other 2 features have little impact and theoretically could be removed as a part of your data reduction effort.

Also, it is important to point out that


  When performing PCA, it is typically a good idea to normalize the data
  first. Because PCA seeks to identify the principal components with the
  highest variance, if the data are not properly normalized, attributes
  with large values and large variances (in absolute terms) will end up
  dominating the first principal component when they should not.


IOW, if you didn't normalize your data then your PCA analysis is quite likely meaningless.  

*The above quoted text is from http://www.lauradhamilton.com/introduction-to-principal-component-analysis-pca